We recently published a proof-of-concept demonstration of something called Unsupervised Behavioral Learning (UBL), something we have been researching before and finally got in a good enough condition to do some navigation tasks.
So, we made a little T-maze "rat" demo, where a tiny robot (the "rat") must figure out how to navigate in the T-maze given various "goal states". UBL is a method that allows us to specify arbitrary goal states and the rat will attempt to match them as well as it can. It can be trained completely passively, so we trained it by driving around the maze semi-randomly, and then testing various goal states.
If you want to try it out: The branch "goal4_nodestride" was used to make the video.
Here is the video of that:
Also, we helped out James Bruton in one of his amazing projects on YouTube with AOgmaNeo!